from itertools import product

import torch
from torch.utils import benchmark

# Compare takes a list of measurements which we'll save in results.
results = []

sizes = [1, 64, 1024]
for b, n in product(sizes, sizes):
    # label and sub_label are the rows
    # description is the column
    label = "get_perspective_transform"
    sub_label = f"[{b}, {4}, {2}]"
    x = torch.rand((b, 4, 2))
    for num_threads in [1, 4, 16, 32]:
        results.append(
            benchmark.Timer(
                stmt="get_perspective_transform(x, x)",
                setup="from kornia.geometry import get_perspective_transform",
                globals={"x": x},
                num_threads=num_threads,
                label=label,
                sub_label=sub_label,
                description="get_perspective_transform",
            ).blocked_autorange(min_run_time=1)
        )

compare = benchmark.Compare(results)
compare.print()
